Senior Data Scientist, Recommender Systems {Cincinnati}

Job Description G2 - Senior Data Scientist, Relevancy Team - Personalization & Loyalty Strategy Relevancy Team is responsible for making relevant and personalized customer experiences for Kroger's E-commerce site, which ranks among the top 10 ecommerce companies in the US. We deliver trillions of recommendations to the Kroger website at scale and make them available to millions of Kroger customers. The team has a rich portfolio of sciences which include product and coupon recommender systems, substitute recommendations, and shoppable recipes. We are seeking a talented and experienced senior data scientist to join our data science team, specialized in building search and recommender systems. The ideal candidate will have proven track record of developing deep learning models, expertise in ML frameworks such as TensorFlow or PyTorch, and a strong understanding of various recommendation models and techniques. Requirements 2+ years of proven experience building deep learning models for large-scale recommender systems. Proficiency in ML frameworks such as TensorFlow or PyTorch. Proficiency in SQL, Python and Spark for data analysis and manipulation. Experience working with Databricks is a plus. Proficiency with statistics, design of experiments, exploratory data analysis, and insights generation. Experience working with cloud platforms like Azure or GCP. Experience working with Data Engineering and MLOps is desirable. High level of independence to develop and own toolkits, pipelines, and dashboards. Excellent problem-solving skills and a proactive approach to addressing challenges. Strong analytical and critical thinking skills with attention to detail. Prior experience in the retail or e-commerce industry is a plus. Must be able to learn from others and teach others and work collaboratively as part of a highly interdependent team. Ability to communicate complex ideas effectively to both technical and non-technical stakeholders. Key Responsibilities Design, develop, and implement recommender systems tailored to grocery retail and e-commerce personalization needs. Build advanced machine learning and deep learning models to deliver personalized product, coupon, substitute, and recipe recommendations. Define evaluation methods and key metrics to measure recommender system performance and identify areas for improvement. Conduct A/B testing and offline model evaluations to compare recommendation strategies and improve model outcomes. Perform root cause analysis and model interpretability reviews to understand recommendation results and improve accuracy. Improve personalization by incorporating customer preferences, dietary needs, shopping behaviors, and engagement patterns. Explore recommendation diversity strategies that expose customers to a broader range of relevant products while maintaining accuracy. Partner with ML engineers to support model deployment, serving, versioning, and production pipeline best practices. Collaborate with data scientists, data engineers, full stack engineers, product teams, and business stakeholders to deliver data science solutions. Integrate transactional, customer, product, demographic, and user feedback data to support model development and analytics. Build customer analytics pipelines, reporting dashboards, and performance tracking to monitor recommendation effectiveness over time. Document best practices, technical insights, lessons learned, and model development approaches for internal knowledge sharing. Contribute to internal tools, libraries, and documentation that support adoption and maintenance of recommender system solutions. Participate in knowledge-sharing sessions and technical discussions to support continuous learning across the team.